Gastos Casa
29 agosto, 2022
1. Gastos (cálculos antiguos)
Gastos_casa %>%
dplyr::select(-Tiempo,-link) %>%
dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>%
knitr::kable(.,format = "html", format.args = list(decimal.mark = ",", big.mark = "."),
caption="Tabla 1. Gastos Casa (últimos 30 registros)", align =rep('c', 3)) %>%
kableExtra::kable_styling(bootstrap_options = c("striped", "hover"),font_size = 8) %>%
kableExtra::scroll_box(width = "100%", height = "300px")| fecha | gasto | monto | gastador | obs |
|---|---|---|---|---|
| 11/7/2022 | Comida | 60.660 | Tami | NA |
| 14/7/2022 | Enceres | 18.990 | Andrés | NA |
| 15/7/2022 | Ropa | 18.990 | Andrés | NA |
| 15/7/2022 | Ropa | 18.990 | Andrés | NA |
| 15/7/2022 | Comida | 15.000 | Andrés | NA |
| 19/7/2022 | Parafina | 22.521 | Tami | NA |
| 20/7/2022 | VTR | 21.990 | Andrés | NA |
| 21/7/2022 | Comida | 24.660 | Andrés | NA |
| 23/7/2022 | Enceres | 14.315 | Andrés | NA |
| 23/7/2022 | Comida | 22.263 | Andrés | NA |
| 20/7/2022 | Comida | 41.830 | Andrés | NA |
| 25/7/2022 | Comida | 61.470 | Tami | NA |
| 25/7/2022 | Comida | 16.100 | Tami | NA |
| 25/7/2022 | Cortina baño | 29.120 | Tami | NA |
| 28/7/2022 | Electricidad | 78.798 | Andrés | NA |
| 29/7/2022 | Netflix | 8.320 | Tami | NA |
| 30/7/2022 | Comida | 36.170 | Tami | NA |
| 31/7/2022 | Parafina | 22.060 | Tami | NA |
| 1/8/2022 | Comida | 11.670 | Andrés | NA |
| 8/8/2022 | Comida | 17.890 | Tami | NA |
| 8/8/2022 | Comida | 41.390 | Tami | NA |
| 19/8/2022 | VTR | 21.990 | Andrés | NA |
| 18/8/2022 | Comida | 21.860 | Andrés | NA |
| 19/8/2022 | Comida | 5.213 | Andrés | NA |
| 22/8/2022 | Parafina | 23.300 | Tami | NA |
| 24/8/2022 | Comida | 57.780 | Tami | NA |
| 26/8/2022 | mantencion toyotomi | 34.000 | Andrés | mantencion toyotomi |
| 27/8/2022 | Comida | 19.410 | Tami | NA |
| 31/3/2019 | Comida | 9.000 | Andrés | NA |
| 8/9/2019 | Comida | 24.588 | Andrés | Super Lider |
#para ver las diferencias depués de la diosi
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::group_by(gastador, fecha,.drop = F) %>%
dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>%
dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
#dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv)
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")
par(mfrow=c(1,2))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))
library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
# dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
#dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>%
dplyr::group_by(gastador_nombre, fecha_simp) %>%
dplyr::summarise(monto_total=sum(monto)) %>%
dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
ggplot(aes(hover_css = "fill:none;")) +#, ) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
# guides(color = F)+
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
# x <- girafe(ggobj = gg)
# x <- girafe_options(x = x,
# opts_hover(css = "stroke:red;fill:orange") )
# if( interactive() ) print(x)
#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"
#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )
x <- girafe(ggobj = gg)
x <- girafe_options(x,
opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))xplot<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
dplyr::group_by(month)%>%
dplyr::summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = month, y = gasto_total)) +
geom_point()+
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Mes") +
scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot) plot2<-Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto)/1000) %>%
ggplot2::ggplot(aes(x = day, y = gasto_total)) +
geom_line(size=1) +
sjPlot::theme_sjplot2() +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") +
ggtitle( "Figura. Suma de Gastos por Día") +
scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
theme(axis.text.x = element_text(vjust = 0.5,angle = 45))
plotly::ggplotly(plot2) tsData <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(day)%>%
summarise(gasto_total=sum(monto))%>%
dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
theme_bw()+ labs(x="Weeks")
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
#it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
#ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan.
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
itsa_metro_region_quar2<-
its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
interrupt_var = "covid",
alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 
print(itsa_metro_region_quar2)## [[1]]
## [1] "ITSA Model Fit"
##
## $aov.result
## Anova Table (Type II tests)
##
## Response: depvar
## Sum Sq Df F value Pr(>F)
## interrupt_var 3.9412e+08 2 4.416 0.0126 *
## lag_depvar 7.5788e+10 1 1698.329 <2e-16 ***
## Residuals 2.1420e+10 480
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## $tukey.result
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
##
## $`x$interrupt_var`
## diff lwr upr p adj
## 1-0 7228.838 877.7398 13579.94 0.0210061
## 2-0 27579.411 21711.4003 33447.42 0.0000000
## 2-1 20350.573 16784.9138 23916.23 0.0000000
##
##
## $data
## depvar interrupt_var lag_depvar
## 2 19269.29 0 16010.00
## 3 24139.00 0 19269.29
## 4 23816.14 0 24139.00
## 5 26510.14 0 23816.14
## 6 23456.71 0 26510.14
## 7 24276.71 0 23456.71
## 8 18818.71 0 24276.71
## 9 18517.14 0 18818.71
## 10 15475.29 0 18517.14
## 11 16365.29 0 15475.29
## 12 12621.29 0 16365.29
## 13 12679.86 0 12621.29
## 14 13440.71 0 12679.86
## 15 15382.86 0 13440.71
## 16 13459.71 0 15382.86
## 17 14644.14 0 13459.71
## 18 13927.00 0 14644.14
## 19 22034.57 0 13927.00
## 20 20986.00 0 22034.57
## 21 20390.57 0 20986.00
## 22 22554.14 0 20390.57
## 23 21782.57 0 22554.14
## 24 22529.57 0 21782.57
## 25 24642.71 0 22529.57
## 26 17692.29 0 24642.71
## 27 19668.29 0 17692.29
## 28 28640.00 0 19668.29
## 29 28706.00 0 28640.00
## 30 28331.57 0 28706.00
## 31 25617.86 0 28331.57
## 32 27223.29 0 25617.86
## 33 31622.57 0 27223.29
## 34 32021.43 0 31622.57
## 35 33634.57 0 32021.43
## 36 30784.86 0 33634.57
## 37 34770.57 0 30784.86
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## 271 54407.43 2 49751.43
## 272 54746.29 2 54407.43
## 273 61634.57 2 54746.29
## 274 58926.43 2 61634.57
## 275 69999.29 2 58926.43
## 276 63044.86 2 69999.29
## 277 63285.29 2 63044.86
## 278 61395.43 2 63285.29
## 279 67969.43 2 61395.43
## 280 60792.57 2 67969.43
## 281 56859.14 2 60792.57
## 282 44899.43 2 56859.14
## 283 43064.14 2 44899.43
## 284 62790.29 2 43064.14
## 285 69120.71 2 62790.29
## 286 69589.43 2 69120.71
## 287 66633.29 2 69589.43
## 288 65588.57 2 66633.29
## 289 70168.57 2 65588.57
## 290 74644.71 2 70168.57
## 291 52891.00 2 74644.71
## 292 41560.57 2 52891.00
## 293 34704.86 2 41560.57
## 294 46520.00 2 34704.86
## 295 50231.00 2 46520.00
## 296 49216.71 2 50231.00
## 297 76914.86 2 49216.71
## 298 83720.71 2 76914.86
## 299 84485.00 2 83720.71
## 300 89765.00 2 84485.00
## 301 87702.86 2 89765.00
## 302 82013.86 2 87702.86
## 303 85982.43 2 82013.86
## 304 57248.43 2 85982.43
## 305 52968.43 2 57248.43
## 306 52601.86 2 52968.43
## 307 45493.29 2 52601.86
## 308 42298.86 2 45493.29
## 309 46423.71 2 42298.86
## 310 37898.00 2 46423.71
## 311 36435.14 2 37898.00
## 312 30209.57 2 36435.14
## 313 34541.86 2 30209.57
## 314 33604.71 2 34541.86
## 315 37990.71 2 33604.71
## 316 35683.43 2 37990.71
## 317 65201.86 2 35683.43
## 318 62730.57 2 65201.86
## 319 64589.14 2 62730.57
## 320 73744.86 2 64589.14
## 321 76477.71 2 73744.86
## 322 105647.43 2 76477.71
## 323 103790.29 2 105647.43
## 324 76122.29 2 103790.29
## 325 74746.14 2 76122.29
## 326 72865.71 2 74746.14
## 327 63652.57 2 72865.71
## 328 60358.29 2 63652.57
## 329 25957.14 2 60358.29
## 330 30178.43 2 25957.14
## 331 30681.57 2 30178.43
## 332 33337.29 2 30681.57
## 333 32582.71 2 33337.29
## 334 39184.43 2 32582.71
## 335 40415.71 2 39184.43
## 336 34975.43 2 40415.71
## 337 34076.14 2 34975.43
## 338 34221.14 2 34076.14
## 339 28862.57 2 34221.14
## 340 35729.86 2 28862.57
## 341 36489.29 2 35729.86
## 342 36785.14 2 36489.29
## 343 37787.71 2 36785.14
## 344 39832.14 2 37787.71
## 345 41917.86 2 39832.14
## 346 41633.57 2 41917.86
## 347 33557.00 2 41633.57
## 348 22759.57 2 33557.00
## 349 28877.86 2 22759.57
## 350 27574.00 2 28877.86
## 351 27104.71 2 27574.00
## 352 24376.14 2 27104.71
## 353 29732.29 2 24376.14
## 354 34030.00 2 29732.29
## 355 39139.71 2 34030.00
## 356 37066.57 2 39139.71
## 357 38509.29 2 37066.57
## 358 40957.29 2 38509.29
## 359 49423.00 2 40957.29
## 360 50053.29 2 49423.00
## 361 50284.14 2 50053.29
## 362 53103.86 2 50284.14
## 363 50223.00 2 53103.86
## 364 49587.14 2 50223.00
## 365 41167.71 2 49587.14
## 366 37958.71 2 41167.71
## 367 33582.29 2 37958.71
## 368 31039.43 2 33582.29
## 369 26526.57 2 31039.43
## 370 34869.43 2 26526.57
## 371 37487.43 2 34869.43
## 372 46514.43 2 37487.43
## 373 39613.43 2 46514.43
## 374 38980.57 2 39613.43
## 375 37306.14 2 38980.57
## 376 36771.29 2 37306.14
## 377 26317.00 2 36771.29
## 378 31580.71 2 26317.00
## 379 23626.57 2 31580.71
## 380 33035.71 2 23626.57
## 381 44864.57 2 33035.71
## 382 48946.14 2 44864.57
## 383 46969.57 2 48946.14
## 384 49249.57 2 46969.57
## 385 56370.14 2 49249.57
## 386 67228.71 2 56370.14
## 387 59457.29 2 67228.71
## 388 53124.71 2 59457.29
## 389 52814.14 2 53124.71
## 390 61262.00 2 52814.14
## 391 61861.14 2 61262.00
## 392 71784.71 2 61861.14
## 393 59313.29 2 71784.71
## 394 61107.00 2 59313.29
## 395 60603.43 2 61107.00
## 396 60012.57 2 60603.43
## 397 58280.43 2 60012.57
## 398 56862.71 2 58280.43
## 399 41704.43 2 56862.71
## 400 51533.00 2 41704.43
## 401 50388.71 2 51533.00
## 402 49205.29 2 50388.71
## 403 56533.29 2 49205.29
## 404 47996.14 2 56533.29
## 405 47207.57 2 47996.14
## 406 45292.00 2 47207.57
## 407 40343.43 2 45292.00
## 408 39004.86 2 40343.43
## 409 36788.43 2 39004.86
## 410 30027.57 2 36788.43
## 411 39040.14 2 30027.57
## 412 42390.14 2 39040.14
## 413 36291.14 2 42390.14
## 414 30668.29 2 36291.14
## 415 47693.00 2 30668.29
## 416 52094.43 2 47693.00
## 417 56592.57 2 52094.43
## 418 47971.43 2 56592.57
## 419 43762.43 2 47971.43
## 420 42246.71 2 43762.43
## 421 46352.43 2 42246.71
## 422 33094.86 2 46352.43
## 423 32784.86 2 33094.86
## 424 26212.43 2 32784.86
## 425 32611.57 2 26212.43
## 426 42144.86 2 32611.57
## 427 50034.86 2 42144.86
## 428 46332.00 2 50034.86
## 429 42976.29 2 46332.00
## 430 39456.29 2 42976.29
## 431 39328.29 2 39456.29
## 432 35296.14 2 39328.29
## 433 30875.43 2 35296.14
## 434 27709.00 2 30875.43
## 435 29513.29 2 27709.00
## 436 31630.43 2 29513.29
## 437 29346.14 2 31630.43
## 438 34916.86 2 29346.14
## 439 42020.86 2 34916.86
## 440 38303.00 2 42020.86
## 441 37966.43 2 38303.00
## 442 41408.14 2 37966.43
## 443 38988.14 2 41408.14
## 444 43555.29 2 38988.14
## 445 38114.00 2 43555.29
## 446 27847.86 2 38114.00
## 447 26517.00 2 27847.86
## 448 39518.29 2 26517.00
## 449 39153.71 2 39518.29
## 450 45623.14 2 39153.71
## 451 40627.43 2 45623.14
## 452 41027.71 2 40627.43
## 453 42882.86 2 41027.71
## 454 47139.43 2 42882.86
## 455 35547.57 2 47139.43
## 456 41099.00 2 35547.57
## 457 35859.57 2 41099.00
## 458 44524.57 2 35859.57
## 459 48554.29 2 44524.57
## 460 51554.29 2 48554.29
## 461 47810.29 2 51554.29
## 462 50490.00 2 47810.29
## 463 50720.71 2 50490.00
## 464 52720.71 2 50720.71
## 465 52145.57 2 52720.71
## 466 55515.57 2 52145.57
## 467 52457.00 2 55515.57
## 468 58239.57 2 52457.00
## 469 50523.57 2 58239.57
## 470 47788.57 2 50523.57
## 471 46170.00 2 47788.57
## 472 42305.57 2 46170.00
## 473 46605.57 2 42305.57
## 474 55149.57 2 46605.57
## 475 48769.57 2 55149.57
## 476 50719.43 2 48769.57
## 477 44753.71 2 50719.43
## 478 42898.00 2 44753.71
## 479 46141.14 2 42898.00
## 480 34022.57 2 46141.14
## 481 26651.86 2 34022.57
## 482 28791.86 2 26651.86
## 483 31879.00 2 28791.86
## 484 33584.71 2 31879.00
## 485 34690.43 2 33584.71
##
## $alpha
## [1] 0.05
##
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
##
## $group.means
## interrupt_var count mean s.d.
## 1 0 37 22066.04 6308.636
## 2 1 120 29463.10 9187.258
## 3 2 328 49813.67 16195.319
##
## $dependent
## [1] 19269.29 24139.00 23816.14 26510.14 23456.71 24276.71 18818.71
## [8] 18517.14 15475.29 16365.29 12621.29 12679.86 13440.71 15382.86
## [15] 13459.71 14644.14 13927.00 22034.57 20986.00 20390.57 22554.14
## [22] 21782.57 22529.57 24642.71 17692.29 19668.29 28640.00 28706.00
## [29] 28331.57 25617.86 27223.29 31622.57 32021.43 33634.57 30784.86
## [36] 34770.57 38443.00 35073.00 31422.29 30103.29 19319.29 27926.29
## [43] 30715.43 31962.29 39790.14 39211.57 44548.57 49398.00 41039.00
## [50] 34821.29 29123.57 21275.71 28476.14 24561.86 20323.57 25370.00
## [57] 26811.86 27151.86 27623.29 22896.57 41889.29 44000.14 38558.00
## [64] 43373.86 49001.00 61213.29 58939.57 42046.86 39191.71 42646.43
## [71] 36121.57 30915.57 20273.43 23938.29 19274.29 21662.29 15819.00
## [78] 18126.14 17240.71 16127.71 13917.14 15379.86 19510.14 24567.29
## [85] 25700.43 25729.00 26435.00 31157.14 29818.43 30962.43 28746.71
## [92] 27830.71 28252.14 28717.57 21365.43 24816.86 16838.57 15529.14
## [99] 13286.29 13629.43 14404.86 19524.86 18475.71 22495.00 22254.57
## [106] 24173.29 27466.43 24602.43 20531.14 20846.43 23875.71 36312.71
## [113] 34244.00 36347.43 39779.71 42018.71 39372.57 33444.00 29255.86
## [120] 31640.14 29671.14 31023.71 39723.43 39314.14 38239.86 34649.43
## [127] 36688.43 42867.57 42226.86 32155.14 33603.00 37254.43 33145.57
## [134] 31299.43 30252.00 26310.71 27929.86 27666.14 25017.57 27335.00
## [141] 25760.71 18436.86 21906.00 19418.14 22826.14 23444.29 25264.86
## [148] 25473.29 27366.86 28855.86 32326.86 27141.43 26297.71 23499.14
## [155] 30246.29 39931.86 38020.43 35004.00 40750.86 42363.29 46273.57
## [162] 41083.29 35711.29 41921.71 60583.29 63115.57 61300.14 57666.43
## [169] 55834.00 58927.71 57810.57 48987.14 52219.29 56503.57 56545.00
## [176] 64705.57 53833.29 50114.00 39592.43 29907.29 33923.29 45489.00
## [183] 44866.29 51680.57 58257.00 70600.57 76648.00 69430.14 69651.57
## [190] 77745.14 72795.86 67670.71 55357.86 48524.00 50154.43 45111.57
## [197] 36147.00 43501.57 41472.43 41058.00 41605.57 49382.86 59558.57
## [204] 59134.57 61109.00 63004.43 67344.29 78180.86 69117.86 55597.57
## [211] 49426.14 39119.43 35636.86 39201.14 27777.00 47207.00 55587.29
## [218] 56619.71 82679.86 91259.57 93552.71 102242.71 91884.00 85013.86
## [225] 84535.29 80700.43 79740.57 85163.14 86724.86 80355.00 74875.14
## [232] 81347.00 66062.43 56946.43 47732.14 38129.71 42928.29 45392.57
## [239] 37895.43 30660.29 42430.86 35845.14 40350.43 31494.71 30013.29
## [246] 34197.57 37430.14 26932.43 33729.86 38081.43 44028.00 47139.71
## [253] 46558.86 58350.57 78380.00 78168.29 70510.86 72207.14 67881.00
## [260] 69536.43 62390.71 50113.14 45565.57 45805.29 41348.57 51426.86
## [267] 47160.57 51907.43 49751.43 54407.43 54746.29 61634.57 58926.43
## [274] 69999.29 63044.86 63285.29 61395.43 67969.43 60792.57 56859.14
## [281] 44899.43 43064.14 62790.29 69120.71 69589.43 66633.29 65588.57
## [288] 70168.57 74644.71 52891.00 41560.57 34704.86 46520.00 50231.00
## [295] 49216.71 76914.86 83720.71 84485.00 89765.00 87702.86 82013.86
## [302] 85982.43 57248.43 52968.43 52601.86 45493.29 42298.86 46423.71
## [309] 37898.00 36435.14 30209.57 34541.86 33604.71 37990.71 35683.43
## [316] 65201.86 62730.57 64589.14 73744.86 76477.71 105647.43 103790.29
## [323] 76122.29 74746.14 72865.71 63652.57 60358.29 25957.14 30178.43
## [330] 30681.57 33337.29 32582.71 39184.43 40415.71 34975.43 34076.14
## [337] 34221.14 28862.57 35729.86 36489.29 36785.14 37787.71 39832.14
## [344] 41917.86 41633.57 33557.00 22759.57 28877.86 27574.00 27104.71
## [351] 24376.14 29732.29 34030.00 39139.71 37066.57 38509.29 40957.29
## [358] 49423.00 50053.29 50284.14 53103.86 50223.00 49587.14 41167.71
## [365] 37958.71 33582.29 31039.43 26526.57 34869.43 37487.43 46514.43
## [372] 39613.43 38980.57 37306.14 36771.29 26317.00 31580.71 23626.57
## [379] 33035.71 44864.57 48946.14 46969.57 49249.57 56370.14 67228.71
## [386] 59457.29 53124.71 52814.14 61262.00 61861.14 71784.71 59313.29
## [393] 61107.00 60603.43 60012.57 58280.43 56862.71 41704.43 51533.00
## [400] 50388.71 49205.29 56533.29 47996.14 47207.57 45292.00 40343.43
## [407] 39004.86 36788.43 30027.57 39040.14 42390.14 36291.14 30668.29
## [414] 47693.00 52094.43 56592.57 47971.43 43762.43 42246.71 46352.43
## [421] 33094.86 32784.86 26212.43 32611.57 42144.86 50034.86 46332.00
## [428] 42976.29 39456.29 39328.29 35296.14 30875.43 27709.00 29513.29
## [435] 31630.43 29346.14 34916.86 42020.86 38303.00 37966.43 41408.14
## [442] 38988.14 43555.29 38114.00 27847.86 26517.00 39518.29 39153.71
## [449] 45623.14 40627.43 41027.71 42882.86 47139.43 35547.57 41099.00
## [456] 35859.57 44524.57 48554.29 51554.29 47810.29 50490.00 50720.71
## [463] 52720.71 52145.57 55515.57 52457.00 58239.57 50523.57 47788.57
## [470] 46170.00 42305.57 46605.57 55149.57 48769.57 50719.43 44753.71
## [477] 42898.00 46141.14 34022.57 26651.86 28791.86 31879.00 33584.71
## [484] 34690.43
##
## $interrupt_var
## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2
## Levels: 0 1 2
##
## $residuals
## 2 3 4 5 6
## 2079.613929 4066.394400 -563.867865 2415.708748 -3020.641843
## 7 8 9 10 11
## 500.205291 -5683.108761 -1156.919098 -3932.027467 -351.415602
## 12 13 14 15 16
## -4882.646707 -1512.397461 -803.348464 465.793933 -3175.231248
## 17 18 19 20 21
## -289.726410 -2054.531080 6687.374241 -1532.581924 -1200.518311
## 22 23 24 25 26
## 1489.727154 -1195.586664 233.890896 1686.290342 -7133.275068
## 27 28 29 30 31
## 990.582870 8214.467197 344.722757 -88.084751 -2470.605982
## 32 33 34 35 36
## 1535.182472 4514.419525 1021.979416 2282.321431 -1994.265028
## 37 38 39 40 41
## 4512.105130 4247.241607 -2371.129183 -3040.979621 -1130.815754
## 42 43 44 45 46
## -10748.121566 7397.642746 2573.641646 1353.420122 8078.395549
## 47 48 49 50 51
## 575.854875 6424.618274 6553.313803 -6095.148010 -4919.081613
## 52 53 54 55 56
## -5117.045238 -7925.106972 6216.981432 -4066.294554 -4842.279742
## 57 58 59 60 61
## 3953.036860 931.179912 -4.185023 166.503575 -4977.203109
## 62 63 64 65 66
## 18196.428604 3507.672875 -3801.585040 5828.007087 7195.383045
## 67 68 69 70 71
## 14430.295849 1354.456643 -13527.090665 -1440.132247 4540.039642
## 72 73 74 75 76
## -5040.613619 -4475.186092 -10512.469249 2565.675402 -5339.998237
## 77 78 79 80 81
## 1173.446565 -6782.095193 693.605272 -2232.558829 -2562.371287
## 82 83 84 85 86
## -3788.461569 -370.432015 2466.040009 3869.824005 529.775671
## 87 88 89 90 91
## -443.950995 236.776739 4334.441888 -1181.146263 1146.985789
## 92 93 94 95 96
## -2080.630052 -1036.765776 194.891663 287.554302 -7476.273778
## 97 98 99 100 101
## 2478.340800 -8552.834721 -2805.235563 -3889.864726 -1562.848940
## 102 103 104 105 106
## -1090.940291 3343.170393 -2234.762641 2712.520703 -1083.083975
## 107 108 109 110 111
## 1048.296435 2644.280227 -3132.601223 -4670.594932 -754.137586
## 112 113 114 115 116
## 1996.268666 11753.776598 -1315.828984 2617.438052 4189.179498
## 117 118 119 120 121
## 3392.222109 -1234.381921 -4822.362377 -3766.509912 2322.311005
## 122 123 124 125 126
## -1755.659644 1338.550038 8841.875222 737.437046 25.176551
## 127 128 129 130 131
## -2615.014796 2599.824589 6975.412139 869.064752 -8635.918955
## 132 133 134 135 136
## 1720.664893 4091.421353 -3247.231463 -1458.969658 -873.430722
## 137 138 139 140 141
## -3888.235142 1217.090533 -478.803102 -2894.111509 1766.056177
## 142 143 144 145 146
## -1858.063082 -7789.418334 2157.890986 -3398.524770 2210.057846
## 147 148 149 150 151
## -186.275259 1087.530681 -314.389576 1394.820668 1208.901198
## 152 153 154 155 156
## 3362.837023 -4892.792858 -1149.843476 -3202.124871 6020.436501
## 157 158 159 160 161
## 9737.962168 -3038.353746 -4364.067681 4050.909010 580.073875
## 162 163 164 165 166
## 3064.119222 -5584.928901 -6365.968948 4596.151193 17764.416389
## 167 168 169 170 171
## 3789.994801 -265.314760 -2293.229225 -911.530928 3803.020177
## 172 173 174 175 176
## -50.603710 -7885.886656 3150.837577 4576.198130 828.050323
## 177 178 179 180 181
## 8951.976965 -9138.573540 -3241.003639 -10472.757761 -10851.262076
## 182 183 184 185 186
## 1731.530862 9744.975352 -1107.952460 6257.142305 6806.135289
## 187 188 189 190 191
## 13332.662749 8461.840336 -4105.144759 2500.690166 10398.401529
## 192 193 194 195 196
## -1709.885511 -2457.239984 -10236.757941 -6179.531786 1495.643876
## 197 198 199 200 201
## -4989.375161 -9493.391532 5790.606270 -2743.870737 -1363.462933
## 202 203 204 205 206
## -449.317277 6843.625447 10140.102399 715.384643 3064.853651
## 207 208 209 210 211
## 3213.842238 5877.137214 12874.977705 -5773.287577 -11277.083960
## 212 213 214 215 216
## -5489.423246 -10337.327942 -4703.308273 1941.414019 -12635.444098
## 217 218 219 220 221
## 16899.545347 8093.426149 1713.246199 26860.175702 12388.929246
## 222 223 224 225 226
## 7093.063161 13754.711042 -4290.563135 -1998.119369 3600.152072
## 227 228 229 230 231
## 188.605395 2620.791877 8892.385102 5657.675907 -2093.563329
## 232 233 234 235 236
## -1939.094991 9379.856527 -11629.262374 -7225.610625 -8376.526953
## 237 238 239 240 241
## -9828.649540 3463.551614 1683.360150 -7993.515703 -8597.215800
## 242 243 244 245 246
## 9573.051704 -7424.078260 2906.464910 -9934.306737 -3582.596271
## 247 248 249 250 251
## 1912.056467 1443.504450 -11913.514081 4169.450691 2508.497169
## 252 253 254 255 256
## 4605.976030 2457.773465 -875.486235 11430.013231 21029.324647
## 257 258 259 260 261
## 3100.993296 -4369.167780 4100.338110 -1726.219216 3755.809605
## 262 263 264 265 266
## -4854.179808 -10811.157356 -4498.856736 -236.682134 -4905.430737
## 267 268 269 270 271
## 9114.949486 -4065.875555 4454.636439 -1900.097937 4662.947302
## 272 273 274 275 276
## 883.435877 7471.992510 -1329.041106 12139.247828 -4609.447670
## 277 278 279 280 281
## 1782.376957 -320.146310 7925.487766 -5066.265203 -2651.553123
## 282 283 284 285 286
## -11132.034464 -2388.602095 18960.904808 7842.981424 2712.245957
## 287 288 289 290 291
## -658.488434 911.592345 6415.672774 6840.671293 -18872.322642
## 292 293 294 295 296
## -10960.952858 -7794.570733 10084.652499 3344.812085 -951.961979
## 297 298 299 300 301
## 27643.346343 9949.383699 4693.689144 9297.656012 2565.198285
## 302 303 304 305 306
## -1299.775872 7700.882923 -24543.434910 -3407.369096 11.845014
## 307 308 309 310 311
## -6772.483234 -3679.171871 3271.251041 -8903.020376 -2824.633161
## 312 313 314 315 316
## -7756.264539 2082.721710 -2686.454934 2528.475411 -3658.355952
## 317 318 319 320 321
## 27900.934510 -680.267097 3364.229032 10875.982375 5510.341680
## 322 323 324 325 326
## 32262.763664 4604.152928 -21421.149742 1675.864947 1012.675097
## 327 328 329 330 331
## -6537.173533 -1682.164154 -33169.414671 1480.690940 -1750.017234
## 332 333 334 335 336
## 460.652437 -2642.976174 4626.178673 18.054463 -6511.339581
## 337 338 339 340 341
## -2598.533009 -1658.088417 -7144.916599 4462.182719 -852.704352
## 342 343 344 345 346
## -1228.584055 -487.706948 669.917788 947.275036 -1181.886141
## 347 348 349 350 351
## -9006.998517 -12660.462761 3008.465230 -3707.195086 -3023.180913
## 352 353 354 355 356
## -5336.655363 2432.988956 1993.037781 3301.297722 -3291.537297
## 357 358 359 360 361
## -15.067345 1156.809552 7457.196034 599.309150 272.660091
## 362 363 364 365 366
## 2888.174463 -2486.802673 -574.457172 -8431.451450 -4193.219924
## 367 368 369 370 371
## -5731.193870 -4402.971574 -6666.596988 5668.014667 906.512815
## 372 373 374 375 376
## 7617.815025 -7267.831251 -1796.551477 -2911.199343 -1964.975301
## 377 378 379 380 381
## -11946.164184 2564.672456 -10045.380079 6399.435442 9905.629743
## 382 383 384 385 386
## 3524.230070 -2062.611006 1965.724399 7069.568951 11629.786094
## 387 388 389 390 391
## -5746.367405 -5204.882314 85.891430 8808.458111 1935.223536
## 392 393 394 395 396
## 11328.835534 -9920.283040 2904.775624 814.611297 669.177853
## 397 398 399 400 401
## -540.334530 -425.917652 -14330.193497 8906.326262 -931.619168
## 402 403 404 405 406
## -1102.893462 7271.883822 -7747.089977 -984.308141 -2202.365011
## 407 408 409 410 411
## -5456.557324 -2417.972175 -3450.395055 -8250.756116 6741.991762
## 412 413 414 415 416
## 2120.107981 -6942.065280 -7170.178029 14828.118326 4170.687751
## 417 418 419 420 421
## 4775.637936 -7824.244216 -4407.590488 -2200.320817 3246.087213
## 422 423 424 425 426
## -13643.108928 -2226.398134 -8524.622613 3688.026095 7561.082255
## 427 428 429 430 431
## 7018.611415 -3663.182154 -3743.610686 -4295.382972 -1309.839725
## 432 433 434 435 436
## -5228.762828 -6082.928487 -5339.105607 -734.020945 -212.821724
## 437 438 439 440 441
## -4369.782395 3221.449607 5397.989423 -4603.564091 -1651.581826
## 442 443 444 445 446
## 2087.839760 -3376.457476 3331.246363 -6149.811169 -11602.977211
## 447 448 449 450 451
## -3853.129875 10325.338019 -1539.251972 5252.650721 -5465.462894
## 452 453 454 455 456
## -646.321362 854.757040 3470.400197 -11886.519227 3918.246995
## 457 458 459 460 461
## -6231.582980 7067.845272 3433.112900 2868.712416 -3528.875578
## 462 463 464 465 466
## 2462.516525 322.944925 2118.871372 -225.330149 3653.400578
## 467 468 469 470 471
## -2386.034698 6101.932872 -6728.921168 -2638.892846 -1838.276553
## 472 473 474 475 476
## -4271.031220 3447.169201 8187.693075 -5749.725534 1843.428745
## 477 478 479 480 481
## -5846.991376 -2425.856393 2458.720181 -12528.506231 -9179.988631
## 482 483 484 485
## -520.375651 673.874436 -351.079686 -754.119717
##
## $fitted.values
## 2 3 4 5 6 7 8 9
## 17189.67 20072.61 24380.01 24094.43 26477.36 23776.51 24501.82 19674.06
## 10 11 12 13 14 15 16 17
## 19407.31 16716.70 17503.93 14192.25 14244.06 14917.06 16634.95 14933.87
## 18 19 20 21 22 23 24 25
## 15981.53 15347.20 22518.58 21591.09 21064.42 22978.16 22295.68 22956.42
## 26 27 28 29 30 31 32 33
## 24825.56 18677.70 20425.53 28361.28 28419.66 28088.46 25688.10 27108.15
## 34 35 36 37 38 39 40 41
## 30999.45 31352.25 32779.12 30258.47 34195.76 37444.13 34463.27 31234.10
## 42 43 44 45 46 47 48 49
## 30067.41 20528.64 28141.79 30608.87 31711.75 38635.72 38123.95 42844.69
## 50 51 52 53 54 55 56 57
## 47134.15 39740.37 34240.62 29200.82 22259.16 28628.15 25165.85 21416.96
## 58 59 60 61 62 63 64 65
## 25880.68 27156.04 27456.78 27873.77 23692.86 40492.47 42359.59 37545.85
## 66 67 68 69 70 71 72 73
## 41805.62 46782.99 57585.11 55573.95 40631.85 38106.39 41162.19 35390.76
## 74 75 76 77 78 79 80 81
## 30785.90 21372.61 24614.28 20488.84 22601.10 17432.54 19473.27 18690.09
## 82 83 84 85 86 87 88 89
## 17705.60 15750.29 17044.10 20697.46 25170.65 26172.95 26198.22 26822.70
## 90 91 92 93 94 95 96 97
## 30999.57 29815.44 30827.34 28867.48 28057.25 28430.02 28841.70 22338.52
## 98 99 100 101 102 103 104 105
## 25391.41 18334.38 17176.15 15192.28 15495.80 16181.69 20710.48 19782.48
## 106 107 108 109 110 111 112 113
## 23337.66 23124.99 24822.15 27735.03 25201.74 21600.57 21879.45 24558.94
## 114 115 116 117 118 119 120 121
## 35559.83 33729.99 35590.53 38626.49 40606.95 38266.36 33022.37 29317.83
## 122 123 124 125 126 127 128 129
## 31426.80 29685.16 30881.55 38576.71 38214.68 37264.44 34088.60 35892.16
## 130 131 132 133 134 135 136 137
## 41357.79 40791.06 31882.34 33163.01 36392.80 32758.40 31125.43 30198.95
## 138 139 140 141 142 143 144 145
## 26712.77 28144.95 27911.68 25568.94 27618.78 26226.28 19748.11 22816.67
## 146 147 148 149 150 151 152 153
## 20616.09 23630.56 24177.33 25787.68 25972.04 27646.96 28964.02 32034.22
## 154 155 156 157 158 159 160 161
## 27447.56 26701.27 24225.85 30193.89 41058.78 39368.07 36699.95 41783.21
## 162 163 164 165 166 167 168 169
## 43209.45 46668.21 42077.25 37325.56 42818.87 59325.58 61565.46 59959.66
## 170 171 172 173 174 175 176 177
## 56745.53 55124.69 57861.18 56873.03 49068.45 51927.37 55716.95 55753.59
## 178 179 180 181 182 183 184 185
## 62971.86 53355.00 50065.19 40758.55 32191.75 35744.02 45974.24 45423.43
## 186 187 188 189 190 191 192 193
## 51450.86 57267.91 68186.16 73535.29 67150.88 67346.74 74505.74 70127.95
## 194 195 196 197 198 199 200 201
## 65594.62 54703.53 48658.78 50100.95 45640.39 37710.97 44216.30 42421.46
## 202 203 204 205 206 207 208 209
## 42054.89 42539.23 49418.47 58419.19 58044.15 59790.59 61467.15 65305.88
## 210 211 212 213 214 215 216 217
## 74891.14 66874.66 54915.57 49456.76 40340.17 37259.73 40412.44 30307.45
## 218 219 220 221 222 223 224 225
## 47493.86 54906.47 55819.68 78870.64 86459.65 88488.00 96174.56 87011.98
## 226 227 228 229 230 231 232 233
## 80935.13 80511.82 77119.78 76270.76 81067.18 82448.56 76814.24 71967.14
## 234 235 236 237 238 239 240 241
## 77691.69 64172.04 56108.67 47958.36 39464.73 43709.21 45888.94 39257.50
## 242 243 244 245 246 247 248 249
## 32857.81 43269.22 37443.96 41429.02 33595.88 32285.51 35986.64 38845.94
## 250 251 252 253 254 255 256 257
## 29560.41 35572.93 39422.02 44681.94 47434.34 46920.56 57350.68 75067.29
## 258 259 260 261 262 263 264 265
## 74880.02 68106.80 69607.22 65780.62 67244.89 60924.30 50064.43 46041.97
## 266 267 268 269 270 271 272 273
## 46254.00 42311.91 51226.45 47452.79 51651.53 49744.48 53862.85 54162.58
## 274 275 276 277 278 279 280 281
## 60255.47 57860.04 67654.30 61502.91 61715.57 60043.94 65858.84 59510.70
## 282 283 284 285 286 287 288 289
## 56031.46 45452.74 43829.38 61277.73 66877.18 67291.77 64676.98 63752.90
## 290 291 292 293 294 295 296 297
## 67804.04 71763.32 52521.52 42499.43 36435.35 46886.19 50168.68 49271.51
## 298 299 300 301 302 303 304 305
## 73771.33 79791.31 80467.34 85137.66 83313.63 78281.55 81791.86 56375.80
## 306 307 308 309 310 311 312 313
## 52590.01 52265.77 45978.03 43152.46 46801.02 39259.78 37965.84 32459.14
## 314 315 316 317 318 319 320 321
## 36291.17 35462.24 39341.78 37300.92 63410.84 61224.91 62868.87 70967.37
## 322 323 324 325 326 327 328 329
## 73384.66 99186.13 97543.44 73070.28 71853.04 70189.74 62040.45 59126.56
## 330 331 332 333 334 335 336 337
## 28697.74 32431.59 32876.63 35225.69 34558.25 40397.66 41486.77 36674.68
## 338 339 340 341 342 343 344 345
## 35879.23 36007.49 31267.67 37341.99 38013.73 38275.42 39162.23 40970.58
## 346 347 348 349 350 351 352 353
## 42815.46 42564.00 35420.03 25869.39 31281.20 30127.90 29712.80 27299.30
## 354 355 356 357 358 359 360 361
## 32036.96 35838.42 40358.11 38524.35 39800.48 41965.80 49453.98 50011.48
## 362 363 364 365 366 367 368 369
## 50215.68 52709.80 50161.60 49599.17 42151.93 39313.48 35442.40 33193.17
## 370 371 372 373 374 375 376 377
## 29201.41 36580.92 38896.61 46881.26 40777.12 40217.34 38736.26 38263.16
## 378 379 380 381 382 383 384 385
## 29016.04 33671.95 26636.28 34958.94 45421.91 49032.18 47283.85 49300.57
## 386 387 388 389 390 391 392 393
## 55598.93 65203.65 58329.60 52728.25 52453.54 59925.92 60455.88 69233.57
## 394 395 396 397 398 399 400 401
## 58202.22 59788.82 59343.39 58820.76 57288.63 56034.62 42626.67 51320.33
## 402 403 404 405 406 407 408 409
## 50308.18 49261.40 55743.23 48191.88 47494.37 45799.99 41422.83 40238.82
## 410 411 412 413 414 415 416 417
## 38278.33 32298.15 40270.03 43233.21 37838.46 32864.88 47923.74 51816.93
## 418 419 420 421 422 423 424 425
## 55795.67 48170.02 44447.04 43106.34 46737.97 35011.26 34737.05 28923.55
## 426 427 428 429 430 431 432 433
## 34583.77 43016.25 49995.18 46719.90 43751.67 40638.13 40524.91 36958.36
## 434 435 436 437 438 439 440 441
## 33048.11 30247.31 31843.25 33715.93 31695.41 36622.87 42906.56 39618.01
## 442 443 444 445 446 447 448 449
## 39320.30 42364.60 40224.04 44263.81 39450.83 30370.13 29192.95 40692.97
## 450 451 452 453 454 455 456 457
## 40370.49 46092.89 41674.04 42028.10 43669.03 47434.09 37180.75 42091.15
## 458 459 460 461 462 463 464 465
## 37456.73 45121.17 48685.57 51339.16 48027.48 50397.77 50601.84 52370.90
## 466 467 468 469 470 471 472 473
## 51862.17 54843.03 52137.64 57252.49 50427.46 48008.28 46576.60 43158.40
## 474 475 476 477 478 479 480 481
## 46961.88 54519.30 48876.00 50600.71 45323.86 43682.42 46551.08 35831.85
## 482 483 484 485
## 29312.23 31205.13 33935.79 35444.55
##
## $shapiro.test
## [1] 0
##
## $levenes.test
## [1] 0
##
## $autcorr
## [1] "No autocorrelation evidence"
##
## $post_sums
## [1] "Post-Est Warning"
##
## $adjr_sq
## [1] 0.8562
##
## $fstat.bootstrap
##
## ORDINARY NONPARAMETRIC BOOTSTRAP
##
##
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~
## ., parallel = parr)
##
##
## Bootstrap Statistics :
## original bias std. error
## t1* 4.415965 0.5668222 2.811691
## t2* 1698.329288 29.7566136 252.302030
## WARNING: All values of t3* are NA
##
## $itsa.plot
##
## $booted.ints
## Parameter Lower CI Median F-value Upper CI
## 1 interrupt_var 1.167113 4.559753 10.37938
## 2 lag_depvar 1349.105739 1711.735506 2165.72472Ahora con las tendencias descompuestas
require(zoo)
require(scales)
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>%
dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"electrodomésticos/mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
gasto=="Chromecast"~"electrodomésticos/mantención casa",
gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"electrodomésticos/mantención casa",
gasto=="Sopapo"~"electrodomésticos/mantención casa",
gasto=="filtro agua"~"electrodomésticos/mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Transporte",
gasto=="Uber Reñaca"~"Transporte",
gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
gasto=="Aspiradora"~"electrodomésticos/mantención casa",
gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
gasto=="Pila estufa"~"electrodomésticos/mantención casa",
gasto=="Reloj"~"electrodomésticos/mantención casa",
gasto=="Arreglo"~"electrodomésticos/mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
#dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>%
# dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de diosi. Junio 24, 2019
dplyr::summarise(monto=sum(monto)) %>%
dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>%
ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
#stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
geom_line(size=1) +
facet_grid(gasto~.)+
#geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
scale_y_continuous(labels = f <- function(x) paste0(x/1000)) +
scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
guides(color = F)+
sjPlot::theme_sjplot2() +
theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
theme(
panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black")
)
autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start =
lubridate::decimal_date(as.Date("2019-03-03"))))
# scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start =
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")library(bsts)
library(CausalImpact)
ts_week_covid<-
Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::group_by(fecha_week)%>%
dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
dplyr::ungroup() %>%
dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
dplyr::mutate(covid=as.factor(covid))%>%
data.frame()
ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]## [1] 98.357 4.780 56.784 50.506 64.483 67.248 49.299 35.786 58.503
## [10] 64.083 20.148 73.476 127.004 81.551 69.599 134.446 58.936 26.145
## [19] 129.927 104.989 130.860 81.893 95.697 64.579 303.471 151.106 49.275
## [28] 76.293 33.940 83.071 119.512 20.942 58.055 71.728 44.090 33.740
## [37] 59.264 77.410 60.831 63.376 48.754 235.284 29.604 115.143 72.419
## [46] 5.980 80.063 149.178 69.918 107.601 72.724 63.203 99.681 130.309
## [55] 195.898 112.066# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na,
state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
niter = 20000,
#burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
seed= 2125)## =-=-=-=-= Iteration 0 Mon Aug 29 00:52:52 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Aug 29 00:52:59 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Aug 29 00:53:06 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Aug 29 00:53:13 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Aug 29 00:53:20 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Aug 29 00:53:27 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Aug 29 00:53:34 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Aug 29 00:53:41 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 16000 Mon Aug 29 00:53:48 2022
## =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Aug 29 00:53:55 2022
## =-=-=-=-=#,
# dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")
impact2d1 <- CausalImpact(bsts.model = model1d1,
post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
ylab("Monto Semanal (En miles)")
burn1d1 <- SuggestBurn(0.1, model1d1)corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d <- tm_map(corpus, tolower)
d <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq,
max.words=100, random.order=FALSE, rot.per=0.35,
colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")
fit_month_gasto <- Gastos_casa %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
gasto=="aspiradora"~"electrodomésticos/mantención casa",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
gasto=="Tina"~"Electrodomésticos/ Mantención casa",
gasto=="Nexium"~"Farmacia",
gasto=="donaciones"~"donaciones/regalos",
gasto=="Regalo chocolates"~"donaciones/regalos",
gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
gasto=="Vacuna Influenza"~"Farmacia",
gasto=="Easy"~"Electrodomésticos/ Mantención casa",
gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
gasto=="ropa tami"~"donaciones/regalos",
gasto=="yaz"~"Farmacia",
gasto=="Yaz"~"Farmacia",
gasto=="Remedio"~"Farmacia",
gasto=="Entel"~"VTR",
gasto=="Kerosen"~"Gas/Bencina",
gasto=="Parafina"~"Gas/Bencina",
gasto=="Plata basurero"~"donaciones/regalos",
gasto=="Matri Andrés Kogan"~"donaciones/regalos",
gasto=="Wild Protein"~"Comida",
gasto=="Granola Wild Foods"~"Comida",
gasto=="uber"~"Otros",
gasto=="Uber Reñaca"~"Otros",
gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
gasto=="Pan Pepperino"~"Comida",
gasto=="Cookidoo"~"Comida",
gasto=="remedios"~"Farmacia",
gasto=="Bendina Reñaca"~"Gas/Bencina",
gasto=="Bencina Reñaca"~"Gas/Bencina",
gasto=="Vacunas Influenza"~"Farmacia",
gasto=="Remedios"~"Farmacia",
gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
T~gasto)) %>%
dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>%
dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>%
dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
data.frame() %>% na.omit()
fit_month_gasto_23<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2023",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_22<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_21<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("2021|2022",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame()%>% ungroup()
fit_month_gasto_20<-
fit_month_gasto %>%
#dplyr::filter()
dplyr::filter(grepl("202",fecha_month)) %>%
#sacar el ultimo mes
dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>%
dplyr::group_by(gasto2) %>%
dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>%
data.frame() %>% ungroup()
fit_month_gasto_23 %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>%
janitor::adorn_totals() %>%
#dplyr::select(-3) %>%
knitr::kable(format="html", caption="Tabla. Gastos promedio por ítem a contar del...",
col.names= c("Item","2023","2022","2021","2020")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")| Item | 2023 | 2022 | 2021 | 2020 |
|---|---|---|---|---|
| Agua | NA | 6.281714 | 6.008526 | 7.529516 |
| Comida | NA | 300.780286 | 311.590579 | 343.078226 |
| Comunicaciones | NA | 0.000000 | 0.000000 | 0.000000 |
| Electricidad | NA | 43.067143 | 34.512947 | 29.129064 |
| Enceres | NA | 14.113571 | 14.547842 | 24.017839 |
| Farmacia | NA | 3.140000 | 9.996474 | 11.560452 |
| Gas/Bencina | NA | 61.750000 | 31.329158 | 25.882387 |
| Diosi | NA | 19.003857 | 40.277947 | 39.056032 |
| donaciones/regalos | NA | 0.000000 | 9.056947 | 8.861903 |
| Electrodomésticos/ Mantención casa | NA | 6.761143 | 38.235158 | 26.757032 |
| VTR | NA | 27.990000 | 22.367000 | 21.107677 |
| Netflix | NA | 7.505286 | 7.204316 | 7.608032 |
| Otros | NA | 5.401857 | 1.990158 | 1.219774 |
| Total | 0 | 495.794857 | 527.117053 | 545.807935 |
## Joining, by = "word"
2. UF Proyectada
Saqué la UF proyectada
#options(max.print=5000)
uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
tryCatch(uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table"),
error = function(c) {
uf23b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
tryCatch(uf23 <-uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
error = function(c) {
uf23 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
}
)
uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2023, uf23)
)
uf_serie_corrected<-
uf_serie %>%
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>%
dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>%
dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>%
na.omit()#%>% dplyr::filter(is.na(date3))## Warning: 35 failed to parse.#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)
warning(paste0("number of observations:",nrow(uf_serie_corrected),", min uf: ",min(uf_serie_corrected$value),", min date: ",min(uf_serie_corrected $date3 )))## Warning: number of observations:1713, min uf: 26799.01, min date: 2018-01-01#
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>%
# dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))
ts_uf_proy<-
ts(data = uf_serie_corrected$value,
start = as.numeric(as.Date("2018-01-01")),
end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats <- forecast::tbats(ts_uf_proy)
fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)La proyección de la UF a 298 días más 2022-09-09 00:04:58 sería de: 35.949 pesos// Percentil 95% más alto proyectado: 39.800,62
Ahora con un modelo ARIMA automático
arima_optimal_uf = forecast::auto.arima(ts_uf_proy)
autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq(from = as.Date("2018-01-01"),
to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)),
tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")),
to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
tickmode = "array",
tickangle = 90
))fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="html", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | UF Proyectada (TBATS) | UF Proyectada (ARIMA) |
|---|---|---|
| Lo.95 | 34289.70 | 34237.56 |
| Lo.80 | 34527.79 | 34601.46 |
| Point.Forecast | 35948.95 | 38511.90 |
| Hi.80 | 38010.73 | 43191.23 |
| Hi.95 | 39149.62 | 45668.32 |
3. Gastos proyectados
Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.
Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
"link"),skip=1) %>%
dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>%
dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
data.frame()
uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>% dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
data.frame() %>%
dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found
ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)],
start = 1,
end = nrow(uf_serie_corrected_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)],
start = 1,
end = nrow(Gastos_casa_m),
frequency = 1,
deltat = 1, ts.eps = getOption("ts.eps"))
fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)
seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")
autplo2t<-
autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2tAhora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.
paste0("Optimo pero sin regresor")## [1] "Optimo pero sin regresor"arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal## Series: ts_gastos_casa_m
## ARIMA(1,0,0) with non-zero mean
##
## Coefficients:
## ar1 mean
## 0.3194 989.7393
## s.e. 0.1523 37.5655
##
## sigma^2 = 29175: log likelihood = -274.53
## AIC=555.05 AICc=555.69 BIC=560.27paste0("Optimo pero con regresor")## [1] "Optimo pero con regresor"arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2## Series: ts_gastos_casa_m
## Regression with ARIMA(1,0,0) errors
##
## Coefficients:
## ar1 xreg
## 0.3447 33.4121
## s.e. 0.1547 1.3396
##
## sigma^2 = 29996: log likelihood = -275.12
## AIC=556.24 AICc=556.87 BIC=561.45forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>%
dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>%
dplyr::group_by(ano_m)%>%
dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
data.frame()
autplo2t2<-
autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
predict.colour = "blue", predict.linetype = "dashed")%>%
plotly::layout(showlegend = F,
yaxis = list(title = "Gastos (en miles)"),
xaxis = list(
title="Fecha",
ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]),
tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
tickmode = "array"#"array"
))
autplo2t2fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>%
dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>%
dplyr::arrange(variable) %>%
knitr::kable(format="html", caption="Tabla. Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) %>%
kableExtra::kable_classic(bootstrap_options = c("striped", "hover"),font_size = 12) %>%
kableExtra::scroll_box(width = "100%", height = "375px")## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables| Item | Modelo ARIMA con regresor (UF) | Modelo ARIMA sin regresor | Modelo TBATS |
|---|---|---|---|
| Lo.95 | 946.4493 | 636.4574 | 683.4227 |
| Lo.80 | 1071.6189 | 758.7406 | 765.8957 |
| Point.Forecast | 1308.0699 | 989.7390 | 949.8170 |
| Hi.80 | 1544.5208 | 1220.7375 | 1250.1955 |
| Hi.95 | 1669.6904 | 1343.0207 | 1445.9443 |
Session Info
Sys.getenv("R_LIBS_USER")## [1] "D:\\a\\_temp\\Library"sessionInfo()## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252 LC_CTYPE=Spanish_Chile.1252
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C
## [5] LC_TIME=Spanish_Chile.1252
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] CausalImpact_1.2.7 bsts_0.9.8 BoomSpikeSlab_1.2.5
## [4] Boom_0.9.10 MASS_7.3-54 scales_1.2.1
## [7] ggiraph_0.8.3 tidytext_0.3.4 DT_0.24
## [10] autoplotly_0.1.4 rvest_1.0.3 plotly_4.10.0
## [13] xts_0.12.1 forecast_8.17.0 wordcloud_2.6
## [16] RColorBrewer_1.1-3 SnowballC_0.7.0 tm_0.7-8
## [19] NLP_0.2-1 tsibble_1.1.2 forcats_0.5.2
## [22] dplyr_1.0.9 purrr_0.3.4 tidyr_1.2.0
## [25] tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2
## [28] sjPlot_2.8.11 lattice_0.20-45 gridExtra_2.3
## [31] plotrix_3.8-2 sparklyr_1.7.8 httr_1.4.4
## [34] readxl_1.4.1 zoo_1.8-10 stringr_1.4.1
## [37] stringi_1.7.8 DataExplorer_0.8.2 data.table_1.14.2
## [40] reshape2_1.4.4 fUnitRoots_4021.80 plyr_1.8.7
## [43] readr_2.1.2
##
## loaded via a namespace (and not attached):
## [1] utf8_1.2.2 tidyselect_1.1.2 lme4_1.1-30
## [4] htmlwidgets_1.5.4 munsell_0.5.0 codetools_0.2-18
## [7] effectsize_0.7.0.5 its.analysis_1.6.0 withr_2.5.0
## [10] colorspace_2.0-3 ggfortify_0.4.14 highr_0.9
## [13] knitr_1.40 uuid_1.1-0 rstudioapi_0.14
## [16] TTR_0.24.3 labeling_0.4.2 emmeans_1.8.0
## [19] slam_0.1-50 bit64_4.0.5 farver_2.1.1
## [22] datawizard_0.5.1 fBasics_4021.92 rprojroot_2.0.3
## [25] vctrs_0.4.1 generics_0.1.3 xfun_0.32
## [28] R6_2.5.1 bitops_1.0-7 cachem_1.0.6
## [31] assertthat_0.2.1 networkD3_0.4 vroom_1.5.7
## [34] nnet_7.3-16 googlesheets4_1.0.1 gtable_0.3.0
## [37] spatial_7.3-14 timeDate_4021.104 rlang_1.0.4
## [40] forge_0.2.0 systemfonts_1.0.4 splines_4.1.2
## [43] lazyeval_0.2.2 gargle_1.2.0 selectr_0.4-2
## [46] broom_1.0.0 yaml_2.3.5 abind_1.4-5
## [49] modelr_0.1.9 crosstalk_1.2.0 backports_1.4.1
## [52] quantmod_0.4.20 tokenizers_0.2.1 tools_4.1.2
## [55] ellipsis_0.3.2 gplots_3.1.3 kableExtra_1.3.4
## [58] jquerylib_0.1.4 Rcpp_1.0.9 base64enc_0.1-3
## [61] fracdiff_1.5-1 haven_2.5.1 fs_1.5.2
## [64] magrittr_2.0.3 timeSeries_4021.104 lmtest_0.9-40
## [67] reprex_2.0.2 googledrive_2.0.0 mvtnorm_1.1-3
## [70] sjmisc_2.8.9 hms_1.1.2 evaluate_0.16
## [73] xtable_1.8-4 sjstats_0.18.1 ggeffects_1.1.3
## [76] compiler_4.1.2 KernSmooth_2.23-20 crayon_1.5.1
## [79] minqa_1.2.4 htmltools_0.5.3 tzdb_0.3.0
## [82] lubridate_1.8.0 DBI_1.1.3 sjlabelled_1.2.0
## [85] dbplyr_2.2.1 boot_1.3-28 Matrix_1.3-4
## [88] car_3.1-0 cli_3.3.0 quadprog_1.5-8
## [91] parallel_4.1.2 insight_0.18.2 igraph_1.3.4
## [94] pkgconfig_2.0.3 xml2_1.3.3 svglite_2.1.0
## [97] bslib_0.4.0 webshot_0.5.3 estimability_1.4.1
## [100] anytime_0.3.9 snakecase_0.11.0 janeaustenr_1.0.0
## [103] digest_0.6.29 parameters_0.18.2 janitor_2.1.0
## [106] rmarkdown_2.16 cellranger_1.1.0 curl_4.3.2
## [109] gtools_3.9.3 urca_1.3-0 nloptr_2.0.3
## [112] lifecycle_1.0.1 nlme_3.1-153 jsonlite_1.8.0
## [115] tseries_0.10-51 carData_3.0-5 viridisLite_0.4.1
## [118] fansi_1.0.3 pillar_1.8.1 fastmap_1.1.0
## [121] glue_1.6.2 bayestestR_0.12.1 bit_4.0.4
## [124] sass_0.4.2 performance_0.9.2 r2d3_0.2.6
## [127] caTools_1.18.2#save.image("__analisis.RData")
sesion_info <- devtools::session_info()
dplyr::select(
tibble::as_tibble(sesion_info$packages),
c(package, loadedversion, source)
) %>%
DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
caption = htmltools::tags$caption(
style = 'caption-side: top; text-align: left;',
'', htmltools::em('Paquetes estadísticos utilizados')),
options=list(
initComplete = htmlwidgets::JS(
"function(settings, json) {",
"$(this.api().tables().body()).css({'font-size': '80%'});",
"}")))